Identification of pre-sliding friction dynamics.

The hysteretic nonlinear dependence of pre-sliding friction force on displacement is modeled using different physics-based and black-box approaches including various Maxwell-slip models, NARX models, neural networks, nonparametric (local) models and dynamical networks. The efficiency and accuracy of these identification methods is compared for an experimental time series where the observed friction force is predicted from the measured displacement. All models, although varying in their degree of accuracy, show good prediction capability of pre-sliding friction. Finally, we show that even better results can be achieved by using an ensemble of the best models for prediction.

Short-term effects of air pollution on childhood respiratory illness in Piraeus, Greece, 1987-1992: nonparametric stochastic dynamic analysis.

The short-term effects of air pollution on childhood respiratory illness are analyzed for the city of Piraeus, Greece during the 1987-1992 period using a distinct, nonparametric, stochastic dynamical system approach. This approach accounts for the time continuity of the phenomenon, including contemporaneous, lagged, and unstructured cumulative effects. It also accounts for the presence of autocorrelation and nonstationary structures in the series and aims at revealing the potentially subtle relationships among the examined pollutants (smoke, CO, SO(2), NO(2), O(3)), meteorological factors (temperature, humidity), and hospital admissions. The approach is also capable of assessing the individual effects of any isolated pollutant, as well as the total effects of all measured pollutants. The study is based upon 4042 hospital admissions, and its results indicate statistically significant effects of each individual pollutant, as well as meteorological factor, on hospital admissions. It is interesting that these effects appear stronger for the second half of the examined period, despite declines in the observed mean concentrations for most pollutants. The total effects of the four pollutants (excluding O(3)) are characterized by multiple coherence measures of 18.37 and 21.66% in the first and second halves of the examined period, respectively, and are confirmed as statistically significant.

Identification of dynamic myoelectric signal-to-force models during isometric lumbar muscle contractions.

A 14-muscle myoelectric signal (MES)-driven muscle force prediction model of the L3-L4 cross section is developed which includes a dynamic MES-force relationship and allows for cocontraction. Model parameters are estimated from MES and moments data recorded during rapid exertions in trunk flexion, extension, lateral bending and axial twist. Nine young healthy males participated in the experimental testing. The model used in the parameter estimation is of the output error type. Consistent and physically feasible parameter estimates were obtained by normalizing the RMS MES to maximum exertion levels and using nonlinear constrained optimization to minimize a cost function consisting of the trace of the output error covariance matrix. Model performance was evaluated by comparing measured and MES-predicted moments over a series of slow and rapid exertions. Moment prediction errors were on the order of 25, 30 and 40% during attempted trunk flexion-extensions, lateral bends and axial twists, respectively. The model and parameter estimation methods developed provide a means to estimate lumbar muscle and spine loads, as well as to empirically investigate the use and effects of cocontraction during physical task performances.